
"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a       :						int       		=		logging.get_logger(__name__)

a       :						Optional[int]       		=		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
a       :						Union[str, Any]       		=		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    for attribute in key.split("""."""   ):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                # those layers are only relevant for pretraining and should be dropped
                return

            if attribute == "ctc_proj":
                # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                _UpperCAmelCase        = """lm_head"""

        _UpperCAmelCase        = getattr(_A       ,   _A   )

    if weight_type is not None:
        _UpperCAmelCase        = getattr(_A       ,   _A   ).shape
    else:
        _UpperCAmelCase        = hf_pointer.shape

    assert hf_shape == value.shape, (
        F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
        F""" {value.shape} for {full_name}"""
    )

    if weight_type == "weight":
        _UpperCAmelCase        = value
    elif weight_type == "weight_g":
        _UpperCAmelCase        = value
    elif weight_type == "weight_v":
        _UpperCAmelCase        = value
    elif weight_type == "bias":
        _UpperCAmelCase        = value
    else:
        _UpperCAmelCase        = value

    logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = fairseq_model.state_dict()

    _UpperCAmelCase        = hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        _UpperCAmelCase        = False
        if "conv_layers" in name:
            load_conv_layer(
                _A       ,   _A       ,   _A       ,   _A       ,   hf_model.config.feat_extract_norm == """group"""       ,   )
            _UpperCAmelCase        = True
        else:
            for key, mapped_key in MAPPING.items():
                _UpperCAmelCase        = """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
                    _UpperCAmelCase        = True
                    if "*" in mapped_key:
                        _UpperCAmelCase        = name.split(_A   )[0].split("""."""   )[-2]
                        _UpperCAmelCase        = mapped_key.replace("""*"""       ,   _A   )
                    if "weight_g" in name:
                        _UpperCAmelCase        = """weight_g"""
                    elif "weight_v" in name:
                        _UpperCAmelCase        = """weight_v"""
                    elif "bias" in name:
                        _UpperCAmelCase        = """bias"""
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _UpperCAmelCase        = """weight"""
                    else:
                        _UpperCAmelCase        = None
                    set_recursively(_A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )
                continue
        if not is_used:
            unused_weights.append(_A   )

    logger.warning(F"""Unused weights: {unused_weights}"""   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = full_name.split("""conv_layers."""   )[-1]
    _UpperCAmelCase        = name.split("""."""   )
    _UpperCAmelCase        = int(items[0]   )
    _UpperCAmelCase        = int(items[1]   )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                " found."
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            _UpperCAmelCase        = value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
    else:
        unused_weights.append(_A   )


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=None       ,   _A=None       ,   _A=True   )     ->	int:





    """simple docstring"""

    if config_path is not None:
        _UpperCAmelCase        = UniSpeechConfig.from_pretrained(_A   )
    else:
        _UpperCAmelCase        = UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            _UpperCAmelCase        = Dictionary.load_from_json(_A   )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _UpperCAmelCase        = target_dict.pad_index
            _UpperCAmelCase        = target_dict.bos_index
            _UpperCAmelCase        = target_dict.eos_index
            _UpperCAmelCase        = len(target_dict.symbols   )
            _UpperCAmelCase        = os.path.join(_A       ,   """vocab.json"""   )
            if not os.path.isdir(_A   ):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_A   )   )
                return
            os.makedirs(_A       ,   exist_ok=_A   )
            _UpperCAmelCase        = target_dict.indices

            # fairseq has the <pad> and <s> switched
            _UpperCAmelCase        = 4_2
            _UpperCAmelCase        = 4_3
            with open(_A       ,   """w"""       ,   encoding="""utf-8"""   ) as vocab_handle:
                json.dump(_A       ,   _A   )
            _UpperCAmelCase        = WavaVecaPhonemeCTCTokenizer(
                _A       ,   unk_token=target_dict.unk_word       ,   pad_token=target_dict.pad_word       ,   bos_token=target_dict.bos_word       ,   eos_token=target_dict.eos_word       ,   word_delimiter_token="""|"""       ,   do_lower_case=_A       ,   )
            _UpperCAmelCase        = True if config.feat_extract_norm == """layer""" else False
            _UpperCAmelCase        = WavaVecaFeatureExtractor(
                feature_size=1       ,   sampling_rate=1_6_0_0_0       ,   padding_value=0       ,   do_normalize=_A       ,   return_attention_mask=_A       ,   )
            _UpperCAmelCase        = WavaVecaProcessor(feature_extractor=_A       ,   tokenizer=_A   )
            processor.save_pretrained(_A   )

        _UpperCAmelCase        = UniSpeechForCTC(_A   )
    else:
        _UpperCAmelCase        = UniSpeechForPreTraining(_A   )

    if is_finetuned:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]       ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   ), """w2v_path""": checkpoint_path}   )
    else:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

    _UpperCAmelCase        = model[0].eval()

    recursively_load_weights(_A       ,   _A       ,   _A   )

    hf_unispeech.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						int       		=		argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 a       :						Dict       		=		parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )

"""simple docstring"""



from ....configuration_utils import PretrainedConfig
from ....utils import logging


a       :						Dict       		=		logging.get_logger(__name__)

# TODO: upload to AWS
a       :						Tuple       		=		{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					int			     =					'retribert'







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							Optional[Any]=3_05_22  ,       __UpperCamelCase      :							int=7_68  ,       __UpperCamelCase      :							Any=8  ,       __UpperCamelCase      :							Dict=12  ,       __UpperCamelCase      :							List[Any]=30_72  ,       __UpperCamelCase      :							List[Any]="gelu"  ,       __UpperCamelCase      :							Optional[int]=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							Union[str, Any]=5_12  ,       __UpperCamelCase      :							Dict=2  ,       __UpperCamelCase      :							Tuple=0.0_2  ,       __UpperCamelCase      :							List[str]=1e-12  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							str=1_28  ,       __UpperCamelCase      :							Union[str, Any]=0  ,       **__UpperCamelCase      :							Any  ,       )		->List[Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = share_encoders
        _UpperCAmelCase        = projection_dim